Multi-features ABC inventory classification (MCIC) is targeted to optimize inventory management
through inventory items classification in order to set policies and rules to manage them. A flawed classification
of items may lead to financial losses and customers dissatisfaction. This paper presents a technique
for the identification and the correction of the bias that exists in the ABC items classification done
by inventory experts and decision makers. In this study, the classification familiarity bias is found and
rectified through patterns recognition. A pattern based reclassification is proposed using a multi-class
model based on Logical Analysis of Data (LAD). Accuracy prediction tests are conducted in order to evaluate
the proposed pattern based classification. The results are compared with the classification obtained
based on the Euclidean distance.